Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications

Yuan, Mengfei; Paradiso, Sean; Meredig, Bryce; Niezgoda, Stephen R.

doi:10.1007/s40192-018-0123-x

Machine Learning–Based Reduce Order Crystal Plasticity Modeling for ICME Applications

Journal Article · Mon Dec 17 23:00:00 EST 2018 · Integrating Materials and Manufacturing Innovation

DOI:https://doi.org/10.1007/s40192-018-0123-x· OSTI ID:1780461

Yuan, Mengfei; Paradiso, Sean; Meredig, Bryce; Niezgoda, Stephen R.

Abstract

Crystal plasticity simulation is a widely used technique for studying the deformation processing of polycrystalline materials. However, inclusion of crystal plasticity simulation into design paradigms such as integrated computational materials engineering (ICME) is hindered by the computational cost of large-scale simulations. In this work, we present a machine learning (ML) framework using the material information platform, Open Citrination, to develop and calibrate a reduced order crystal plasticity model for face-centered cubic (FCC) polycrystalline materials, which can be both rapidly exercised and easily inverted. The reduced order model takes crystallographic texture, constitutive model parameters, and loading condition as inputs and returns the stress-strain curve and final texture. The model can also be inverted and take a stress-strain curve, loading condition, and final texture as inputs and return the initial texture and constitutive model parameters as outputs. Principal component analysis (PCA) is used to develop an efficient description of the crystallographic texture. A viscoplastic self-consistent (VPSC) crystal plasticity solver is used to create the training data by modeling the stress-strain behavior and evolution of texture during deformation processing.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: FE0027776

OSTI ID:: 1780461

Journal Information:: Integrating Materials and Manufacturing Innovation, Journal Name: Integrating Materials and Manufacturing Innovation Journal Issue: 4 Vol. 7; ISSN 2193-9764

Publisher:: Springer Science + Business MediaCopyright Statement

Country of Publication:: Germany

Language:: English

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